超越 Flesch-Kincaid：基于提示的指标改进教育文本难度分类
Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts
AI总结：
本文提出基于提示的文本难度指标，利用大语言模型捕捉抽象复杂特征，实验证明其能显著提升教育文本难度分类效果，优于传统静态指标。
摘要：
将大语言模型（LLMs）用于对话式教学等教育应用是一个热门话题。然而，有效的教学要求教师根据学生的教育水平调整内容和解释的难度。即使是当今最优秀的 LLMs 也难以很好地完成这一任务。如果我们希望改进 LLMs 在这一适应性任务上的表现，就需要能够可靠地衡量适应性是否成功。然而，当前用于文本难度的静态指标，如 Flesch-Kincaid 阅读易读性分数，已被认为粗糙且脆弱。因此，我们引入并评估了一套新的基于提示的文本难度指标。基于一项用户研究，我们创建了基于提示的指标作为 LLMs 的输入。这些指标利用 LLMs 的通用语言理解能力来捕捉比静态指标更抽象和复杂的特征。回归实验表明，在仅使用静态指标的基础上加入我们的基于提示的指标，能够显著改善文本难度分类效果。我们的结果展示了利用 LLMs 评估文本对不同教育水平适应性的前景。
英文摘要：
Using large language models (LLMs) for educational applications like dialogue-based teaching is a hot topic. Effective teaching, however, requires teachers to adapt the difficulty of content and explanations to the education level of their students. Even the best LLMs today struggle to do this well. If we want to improve LLMs on this adaptation task, we need to be able to measure adaptation success reliably. However, current Static metrics for text difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude and brittle. We, therefore, introduce and evaluate a new set of Prompt-based metrics for text difficulty. Based on a user study, we create Prompt-based metrics as inputs for LLMs. They leverage LLM's general language understanding capabilities to capture more abstract and complex features than Static metrics. Regression experiments show that adding our Prompt-based metrics significantly improves text difficulty classification over Static metrics alone. Our results demonstrate the promise of using LLMs to evaluate text adaptation to different education levels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
